1. Field of the Invention
The present invention relates to predictive modeling, and specifically to reducing the time and resources required for developing, deploying, and executing a model.
2. Background of Invention
Direct marketing campaigns involve targeting a set of existing customers for a promotional offer. For example, credit card customers that charge a certain amount on travel purchases per month can be offered a credit card with travel rewards, because it is likely that they will accept the offer. Decision sciences and predictive modeling are used to estimate the likelihood (referred to herein as a score) that a particular customer will accept an offer. Thus, the effectiveness of a direct marketing campaign is related to the robustness of the predictive model on which it was based.
Because models are so integral to direct marketing campaigns, it is desirable to execute multiple models efficiently while reducing the time and effort it takes for model development and model deployment. In existing systems, there are deficiencies in model development, model deployment, and model execution. These deficiencies increase the time and cost of developing a model and how effectively a model can be executed.
In existing systems, data is stored in multiple disparate sources of customer data each having their own unique definitions and access requirements. When data is stored in multiple disparate sources, a modeler needs to standardize data and create data tables before data can be analyzed. This results in inconsistent results, compliance risks, and an overall low confidence in the outcomes.
In existing systems, implementing the model logic in the development phase is not a seamless process. In existing systems, models are often developed in one programming language and deployed in another programming language. Thus, implementing a model requires converting logic code into system compatible code. Converting model logic into a model that can be implemented requires using resources with high technical skills to translate logic into system compatible code. This can result in numerous errors and prolonged implementations which increases the time it takes for the deployment phase to be completed.
In some existing systems, model execution occurs at a mainframe location and is based on billing cycles. That is, customers are scored at the end of their billing cycle. Because customer billing cycles vary across a month (e.g. customer A's billing cycle ends the 15th and customer B's billing cycle ends the 30th), it takes a full month to score a customer base. This is problematic because customer scores may change daily. Further, scores for the customers at the beginning of the billing cycle may be obsolete by the time the entire customer base is scored.
What is needed is an end-to-end integrated process of model development, model deployment, and model execution for customer marketing campaigns that enables rapid development and dynamic execution of models.